let perceptron;

//{!1} An array for training data

let training = [];

// A counter to track training data points one by one

let count = 0;

​

//{!3} The formula for a line

function f(x) {

  return 0.5 * x + 1;

}

​

function setup() {

  createCanvas(640, 240);

​

  // Perceptron has 3 inputs (including bias) and learning rate of 0.01

  perceptron = new Perceptron(3, 0.0001);

​

  //{!1} Make 2,000 training data points.

  for (let i = 0; i < 2000; i++) {

    let x = random(-width / 2, width / 2);

    let y = random(-height / 2, height / 2);

    training[i] = [x, y, 1];

  }

}

​

function draw() {

  background(255);

  // Re-orient canvas to match traditional Cartesian plane

  translate(width / 2, height / 2);

  scale(1, -1);

​

  // Draw the line

  stroke(0);

  strokeWeight(2);

  line(-width / 2, f(-width / 2), width / 2, f(width / 2));

  // Get the current (x,y) of the training data

  let x = training[count][0];

  let y = training[count][1];

  // What is the desired output?

  let desired = -1;

  if (y > f(x)) {

    desired = 1;

  }

  // Train the perceptron

  perceptron.train(training[count], desired);

​

  //For animation, training one point at a time.

  count = (count + 1) % training.length;

​

  // Draw all the points and color according to the output of the perceptron

  for (let dataPoint of training) {

    let guess = perceptron.feedforward(dataPoint);

    if (guess > 0) {

      fill(127);

    } else {

      fill(255);

    }

    strokeWeight(1);

    stroke(0);

    circle(dataPoint[0], dataPoint[1], 8);

  }

}